AutoVIML: apprentissage automatique automatisé

La traduction de l'article a été préparée spécialement pour les étudiants du cours "Industrial ML on Big Data"


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, , , Sklearn, , , , . , AutoVIML Python.

AutoVIML – Python, . , . AutoVIML, , , , – , , .. .

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AutoVIML

python-, autoviml pip.

pip install autoviml

AutoVIML

from autoviml.Auto_ViML import Auto_ViML

AutoVIML . , Kaggle. . 

import pandas as pd
df = pd.read_csv('heart_d.csv')
df
Ensemble de données utilisé (source: par l'auteur)
Dataset Used(Source: By Author)

, autoviml , AutoVIML.

#Basic Example with all parameters
model, features, trainm, testm = Auto_ViML(
    train,
    target,
    test,
    sample_submission,
    hyper_param="GS",
    feature_reduction=True,
    scoring_parameter="weighted-f1",
    KMeans_Featurizer=False,
    Boosting_Flag=False,
    Binning_Flag=False,
    Add_Poly=False,
    Stacking_Flag=False,
    Imbalanced_Flag=False,
    verbose=0,
)

, AutoVIML, . , .

  1. train: , dataframe, dataframe. dataframe «df», «df».

  2. target: . «TenYearCHD». 

  3. test: . ( ””), , AutoVIML .

  4. sample_submission: , .

  5. hyper_param: RandomizedSearchCV, , Grid Search CV. «RS».

  6. feature_reduction: true, .

  7. scoring_parameter: , . «Weighted-f1».

  8. KMeans_featurizer: true false XGboost , .

  9. boosting_flag: . false.

  10. binning_flag: false, true, .

  11. add_poly: false.

  12. stacking_flag: false. true, , . false.

  13. Imbalanced_flag: true, SMOTING.

  14. Verbose: . 3.

AutoVIML.

model, features, trainm, testm = Auto_ViML(
    train=df,
    target="TenYearCHD",
    test="",
    sample_submission="",
    hyper_param="RS",
    feature_reduction=True,
    scoring_parameter="weighted-f1",
    KMeans_Featurizer=False,
    Boosting_Flag=True,
    Binning_Flag=False,
    Add_Poly=False,
    Stacking_Flag=True,
    Imbalanced_Flag=True,
    verbose=3
)

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, AutoVIML , , .

, AutoVIML, , . AutoVIML – , .




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